In aftermath of the Financial Crisis, some risk management practitioners advocate wider adoption of Bayesian inference … to replace Value-at-Risk (VaR) models for minimizing risk failures (Borison & Hamm, 2010). They claim reliance of …-Bayesian and [increasingly] Bayesian – continues to be a key methodological foundation of risk management and regulation related …

application, we use our TVC model to estimate the exposures of S&P 500 stocks to market-wide risk factors: we find that a vast … majority of stocks have time-varying risk exposures and that the TVC model helps to forecast these exposures more accurately …

The implied volatility of a European option as a function of strike price and time to maturity forms a volatility … semiparametric models approximate the implied volatility surface (IVS) in a finite dimensional function space, allowing for a low …

as the ratio of the continuous covariation between an asset and observable risk factor (e.g., the market return) and the … estimate of the residual component of the asset returns that is orthogonal (in martingale sense) to the risk factor. Our test … covariation between the risk factor and the estimated residual component of the asset returns over blocks with asymptotically …

The implied volatility of a European option as a function of strike price and time to maturity forms a volatility … semiparametric models approximate the implied volatility surface (IVS) in a finite dimensional function space, allowing for a low … factors with movements in some macroeconomic variables of the Euro-economy. -- Implied volatility surface ; dynamic …

We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model …

Pricing kernels play a major role in quantifying risk aversion and investors' preferences. Several empirical studies …

Pricing kernels play a major role in quantifying risk aversion and investors' preferences. Several empirical studies …. -- Pricing kernels ; risk aversion ; risk neutral density …